Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Mathematical Model For Optimal Decisions In A Representative Democracy
Authors: Malik Magdon-Ismail, Lirong Xia
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a mathematical model for studying representative democracy, in particular understanding the parameters of a representative democracy that gives maximum decision making capability. Our main result states that under general and natural conditions, 1. for fixed voting cost, the optimal number of representatives is linear; 2. for polynomial cost, the optimal number of representatives is logarithmic. |
| Researcher Affiliation | Academia | Malik Magdon-Ismail Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180 EMAIL Lirong Xia Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180 EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or explicitly labeled algorithm blocks. It focuses on mathematical models, theorems, and proofs. |
| Open Source Code | No | The paper does not mention or provide access to any open-source code. |
| Open Datasets | No | The paper describes a mathematical model and theoretical analysis, not experiments on a publicly available dataset. It uses theoretical distributions like "Uniform[a, b]" for its examples. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with dataset splits. |
| Hardware Specification | No | The paper describes a mathematical model and theoretical findings, not computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical mathematical modeling and does not mention any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and presents mathematical proofs and models. It does not describe an experimental setup with hyperparameters or system-level training settings. |